Time series foundation models match the performance of specialized models for day-ahead load forecasting while providing explanations that match domain knowledge on weather and calendar effects.
hub
Moirai 2.0: When less is more for time series forecasting
12 Pith papers cite this work. Polarity classification is still indexing.
12
Pith papers citing it
hub tools
citation-role summary
background 2
baseline 2
citation-polarity summary
representative citing papers
TS-Arena is a live pre-registration platform that evaluates time series forecasts on future data streams to eliminate information leakage.
CTF4Nuclear proposes a common task framework for benchmarking ML methods on nuclear engineering datasets using 12 metrics and a new sparse-measurement system monitoring paradigm.
Structured LLM agents correct agricultural yield forecasts from models like XGBoost, cutting MAE by 20-28% and MASE by up to 66% on strawberry and corn datasets.
Foundation models outperform dataset-specific machine learning in energy time series forecasting across 54 datasets in 9 categories.
WaveMoE uses a dual-path architecture with aligned time-series and wavelet tokens routed through shared experts to improve forecasting performance on diverse benchmarks.
DynLMC creates synthetic time series data with dynamic inter-channel correlations that improve zero-shot forecasting in foundation models across multiple benchmarks.
Timer-S1 is a released 8.3B-parameter MoE time series model that achieves state-of-the-art MASE and CRPS scores on GIFT-Eval using serial scaling and Serial-Token Prediction.
TabPFN-TS captures simple target-covariate relationships more effectively than Chronos-2 in controlled experiments, especially for short horizons.
TimeRFT applies reinforcement learning with multi-faceted step-wise rewards and informative sample selection to improve generalization and accuracy in TSFM adaptation beyond supervised fine-tuning.
Foundation models slightly outperform task-specific models on probabilistic electricity price forecasts but the gap narrows or reverses with extra features or few-shot adaptation, showing that efficiency often outweighs marginal accuracy gains.
The paper envisions AI-native 6G networks anchored by a foundation model and multi-agent systems to shift network management to a unified multi-modal optimization problem.
citing papers explorer
-
Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models
Time series foundation models match the performance of specialized models for day-ahead load forecasting while providing explanations that match domain knowledge on weather and calendar effects.
-
TS-Arena -- A Live Forecast Pre-Registration Platform
TS-Arena is a live pre-registration platform that evaluates time series forecasts on future data streams to eliminate information leakage.
-
CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models
CTF4Nuclear proposes a common task framework for benchmarking ML methods on nuclear engineering datasets using 12 metrics and a new sparse-measurement system monitoring paradigm.
-
Agent-Based Post-Hoc Correction of Agricultural Yield Forecasts
Structured LLM agents correct agricultural yield forecasts from models like XGBoost, cutting MAE by 20-28% and MASE by up to 66% on strawberry and corn datasets.
-
FETS Benchmark: Foundation Models Outperform Dataset-specific Machine Learning in Energy Time Series Forecasting
Foundation models outperform dataset-specific machine learning in energy time series forecasting across 54 datasets in 9 categories.
-
WaveMoE: A Wavelet-Enhanced Mixture-of-Experts Foundation Model for Time Series Forecasting
WaveMoE uses a dual-path architecture with aligned time-series and wavelet tokens routed through shared experts to improve forecasting performance on diverse benchmarks.
-
Dynamic Linear Coregionalization for Realistic Synthetic Multivariate Time Series
DynLMC creates synthetic time series data with dynamic inter-channel correlations that improve zero-shot forecasting in foundation models across multiple benchmarks.
-
Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling
Timer-S1 is a released 8.3B-parameter MoE time series model that achieves state-of-the-art MASE and CRPS scores on GIFT-Eval using serial scaling and Serial-Token Prediction.
-
Investigating simple target-covariate relationships for Chronos-2 and TabPFN-TS
TabPFN-TS captures simple target-covariate relationships more effectively than Chronos-2 in controlled experiments, especially for short horizons.
-
TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning
TimeRFT applies reinforcement learning with multi-faceted step-wise rewards and informative sample selection to improve generalization and accuracy in TSFM adaptation beyond supervised fine-tuning.
-
Assessing the Performance-Efficiency Trade-off of Foundation Models in Probabilistic Electricity Price Forecasting
Foundation models slightly outperform task-specific models on probabilistic electricity price forecasts but the gap narrows or reverses with extra features or few-shot adaptation, showing that efficiency often outweighs marginal accuracy gains.
-
Towards Resilient and Autonomous Networks: A BlueSky Vision on AI-Native 6G
The paper envisions AI-native 6G networks anchored by a foundation model and multi-agent systems to shift network management to a unified multi-modal optimization problem.